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<div class="title">CPartitionGenerator.cs</div>  </div>
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<a href="_c_partition_generator_8cs.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001 ﻿extern alias <a class="code" href="_c_volkovich_yahalom_8cs.html#adb966b3e984a1965fe3340708abd6850">alglibnet2</a>;
<a name="l00002"></a>00002 
<a name="l00003"></a>00003 <span class="keyword">using</span> System;
<a name="l00004"></a>00004 <span class="keyword">using</span> System.Collections.Generic;
<a name="l00005"></a>00005 <span class="keyword">using</span> System.Linq;
<a name="l00006"></a>00006 <span class="keyword">using</span> System.Text;
<a name="l00007"></a>00007 
<a name="l00008"></a>00008 
<a name="l00009"></a>00009 <span class="keyword">namespace </span>ClusterAggregation.Clusterers
<a name="l00010"></a>00010 {
<a name="l00011"></a>00011     <span class="keyword">using</span> Datum;
<a name="l00012"></a>00012     <span class="keyword">using</span> DataSets;
<a name="l00016"></a><a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_partition_generator.html">00016</a>     <span class="keyword">public</span> <span class="keyword">class </span><a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_partition_generator.html">CPartitionGenerator</a>
<a name="l00017"></a>00017     {
<a name="l00019"></a>00019         <span class="keyword">private</span> <a class="code" href="interface_cluster_aggregation_1_1_datum_1_1_i_similarity.html">ISimilarity</a>       m_distFunc;
<a name="l00021"></a>00021         <span class="keyword">private</span> <span class="keywordtype">int</span>               m_numberOfPartitions;
<a name="l00023"></a>00023         <span class="keyword">private</span> <span class="keywordtype">object</span>            m_lock;
<a name="l00025"></a>00025         <span class="keyword">private</span> <span class="keywordtype">double</span>[]          m_smoothingParameters  = <span class="keyword">new</span> <span class="keywordtype">double</span>[] { 0, 0.01, 0.06,0.1, 0.3, 0.7, 1, 0.4 };
<a name="l00027"></a>00027         <span class="keyword">private</span> <a class="code" href="interface_cluster_aggregation_1_1_clusterers_1_1_i_clusterer.html">IClusterer</a>[]      m_clusterers;
<a name="l00029"></a>00029         <span class="keyword">private</span> <a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_k_means_impl.html">CKMeansImpl</a>       m_kmeans;
<a name="l00031"></a>00031         <span class="keyword">private</span> <a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_pam_impl.html">CPamImpl</a>          m_pam;
<a name="l00032"></a>00032 
<a name="l00037"></a><a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_partition_generator.html#a1b040d480fa38efe71edc0283bef8f66">00037</a>         <span class="keyword">public</span> <a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_partition_generator.html">CPartitionGenerator</a>(<a class="code" href="interface_cluster_aggregation_1_1_datum_1_1_i_similarity.html">ISimilarity</a> distFunc = null)
<a name="l00038"></a>00038         {
<a name="l00039"></a>00039             m_distFunc = distFunc;
<a name="l00040"></a>00040             m_kmeans = <span class="keyword">new</span> <a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_k_means_impl.html">CKMeansImpl</a>();
<a name="l00041"></a>00041             m_pam = <span class="keyword">new</span> <a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_pam_impl.html">CPamImpl</a>();
<a name="l00042"></a>00042 
<a name="l00043"></a>00043             <span class="keywordflow">if</span> (distFunc == null)
<a name="l00044"></a>00044             {
<a name="l00045"></a>00045                 m_distFunc = <span class="keyword">new</span> <a class="code" href="class_cluster_aggregation_1_1_datum_1_1_c_euclidean_distance_similarity_function.html">CEuclideanDistanceSimilarityFunction</a>();
<a name="l00046"></a>00046             }
<a name="l00047"></a>00047 
<a name="l00048"></a>00048             m_numberOfPartitions = 5;
<a name="l00049"></a>00049             m_lock = <span class="keyword">new</span> object();
<a name="l00050"></a>00050 
<a name="l00051"></a>00051             m_clusterers = <span class="keyword">new</span> <a class="code" href="interface_cluster_aggregation_1_1_clusterers_1_1_i_clusterer.html">IClusterer</a>[m_smoothingParameters.Length];
<a name="l00052"></a>00052 
<a name="l00053"></a>00053             <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; m_smoothingParameters.Length ; i++)
<a name="l00054"></a>00054             {
<a name="l00055"></a>00055                 m_clusterers[i] = <span class="keyword">new</span> <a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_spectral_clusterer.html">CSpectralClusterer</a>(m_distFunc, m_smoothingParameters[i]);
<a name="l00056"></a>00056             }
<a name="l00057"></a>00057         }
<a name="l00058"></a>00058 
<a name="l00068"></a><a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_partition_generator.html#a0abbaf2e7cab8e84601f161cb8adcdcc">00068</a>         <span class="keyword">public</span> <a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_partition.html">CPartition</a>[] generatePartitions(<a class="code" href="class_cluster_aggregation_1_1_datum_1_1_a_data.html">AData</a>[] arg, <a class="code" href="interface_cluster_aggregation_1_1_datum_1_1_i_similarity.html">ISimilarity</a> distFunc, <span class="keywordtype">int</span> k, <span class="keywordtype">bool</span> useKmeans, <span class="keywordtype">bool</span> usePam, <span class="keywordtype">double</span>[] smoothers)
<a name="l00069"></a>00069         {
<a name="l00070"></a>00070             LinkedList&lt;CPartition&gt; prts = <span class="keyword">new</span> LinkedList&lt;CPartition&gt;();
<a name="l00071"></a>00071 
<a name="l00072"></a>00072             <span class="keywordflow">if</span> (distFunc == null)
<a name="l00073"></a>00073                 distFunc = m_distFunc;
<a name="l00074"></a>00074 
<a name="l00075"></a>00075             <span class="keywordflow">if</span> (useKmeans)
<a name="l00076"></a>00076                 prts.AddFirst(m_kmeans.cluster(arg, distFunc, k));
<a name="l00077"></a>00077 
<a name="l00078"></a>00078             <span class="keywordflow">if</span> (useKmeans)
<a name="l00079"></a>00079                 prts.AddFirst(m_pam.cluster(arg, distFunc, k));
<a name="l00080"></a>00080 
<a name="l00081"></a>00081             <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; smoothers.Length; i++)
<a name="l00082"></a>00082             {
<a name="l00083"></a>00083                 <a class="code" href="interface_cluster_aggregation_1_1_clusterers_1_1_i_clusterer.html">IClusterer</a> tmp = <span class="keyword">new</span> <a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_spectral_clusterer.html">CSpectralClusterer</a>(distFunc, smoothers[i], <span class="keyword">true</span>);
<a name="l00084"></a>00084                 prts.AddFirst(tmp.<a class="code" href="interface_cluster_aggregation_1_1_clusterers_1_1_i_clusterer.html#a17d75056457b54b6a22ad925b92cef9a">cluster</a>(arg, distFunc, k));
<a name="l00085"></a>00085             }
<a name="l00086"></a>00086 
<a name="l00087"></a>00087             <span class="keywordflow">return</span> prts.ToArray();
<a name="l00088"></a>00088         }
<a name="l00089"></a>00089 
<a name="l00090"></a>00090 
<a name="l00100"></a><a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_partition_generator.html#a7169563602ba81a140a965181c152647">00100</a>         <span class="keyword">public</span> <a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_partition.html">CPartition</a>[] generatePartitions(<a class="code" href="class_cluster_aggregation_1_1_datum_1_1_a_data.html">AData</a>[] arg, <a class="code" href="interface_cluster_aggregation_1_1_datum_1_1_i_similarity.html">ISimilarity</a> distFunc, <span class="keywordtype">int</span> k, <span class="keywordtype">bool</span> useKmeans, <span class="keywordtype">bool</span> usePam, <span class="keywordtype">int</span> num_of_smoothers)
<a name="l00101"></a>00101         {
<a name="l00102"></a>00102             LinkedList&lt;CPartition&gt; prts = <span class="keyword">new</span> LinkedList&lt;CPartition&gt;();
<a name="l00103"></a>00103 
<a name="l00104"></a>00104             <span class="keywordflow">if</span> (distFunc == null)
<a name="l00105"></a>00105                 distFunc = m_distFunc;
<a name="l00106"></a>00106 
<a name="l00107"></a>00107             <span class="keywordflow">if</span> (useKmeans)
<a name="l00108"></a>00108                 prts.AddFirst(m_kmeans.cluster(arg, distFunc, k));
<a name="l00109"></a>00109             <span class="keywordflow">if</span> (useKmeans)
<a name="l00110"></a>00110                 prts.AddFirst(m_pam.cluster(arg, distFunc, k));
<a name="l00111"></a>00111 
<a name="l00112"></a>00112             <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; num_of_smoothers; i++)
<a name="l00113"></a>00113             {
<a name="l00114"></a>00114                 prts.AddFirst(m_clusterers[i % m_clusterers.Length].cluster(arg, distFunc, k));
<a name="l00115"></a>00115             }
<a name="l00116"></a>00116 
<a name="l00117"></a>00117             <span class="keywordflow">return</span> prts.ToArray();
<a name="l00118"></a>00118         }
<a name="l00119"></a>00119 
<a name="l00120"></a>00120 
<a name="l00128"></a><a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_partition_generator.html#af044e49312160ec54b0e4db7379f809d">00128</a>         <span class="keyword">public</span> <a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_partition.html">CPartition</a>[] generatePartitions(<a class="code" href="class_cluster_aggregation_1_1_datum_1_1_a_data.html">AData</a>[] arg, <a class="code" href="interface_cluster_aggregation_1_1_datum_1_1_i_similarity.html">ISimilarity</a> distFunc, <span class="keywordtype">int</span> k, <span class="keywordtype">int</span> p)
<a name="l00129"></a>00129         {
<a name="l00130"></a>00130             <span class="keywordflow">if</span> (distFunc == null)
<a name="l00131"></a>00131                 distFunc = m_distFunc;
<a name="l00132"></a>00132             <span class="keywordflow">if</span> (p == 0)
<a name="l00133"></a>00133                 p =  m_numberOfPartitions;
<a name="l00134"></a>00134 
<a name="l00135"></a>00135             <a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_partition.html">CPartition</a>[] partitions = <span class="keyword">new</span> <a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_partition.html">CPartition</a>[p];
<a name="l00136"></a>00136 
<a name="l00137"></a>00137             lock(m_lock)
<a name="l00138"></a>00138             {
<a name="l00139"></a>00139                 <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; p; i++)
<a name="l00140"></a>00140                 {
<a name="l00141"></a>00141                     partitions[i] = m_clusterers[i % m_clusterers.Length].cluster(arg, distFunc, k);
<a name="l00142"></a>00142                 }
<a name="l00143"></a>00143 
<a name="l00144"></a>00144                 <span class="keywordflow">return</span> partitions;
<a name="l00145"></a>00145             } <span class="comment">// end of lock</span>
<a name="l00146"></a>00146         }<span class="comment">//end of func</span>
<a name="l00147"></a>00147     }
<a name="l00148"></a>00148 }
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